MCP Server Plugin

🚀 JetBrains MCP Server Plugin

JetBrains MCP Server Plugin enables seamless integration between Large Language Models (LLMs) and JetBrains IDEs, handling MCP requests and offering extension points for custom tools.

🚀 Quick Start

JetBrains MCP (Model Context Protocol) Server Plugin enables seamless integration between Large Language Models (LLMs) and JetBrains IDEs. This plugin provides the server-side implementation for handling MCP requests and exposes extension points for implementing custom tools.

✨ Features

• Enables seamless integration between LLMs and JetBrains IDEs.
• Provides server - side implementation for MCP requests.
• Exposes extension points for custom tool implementation.

📦 Installation

Prerequisites

• Installation of JetBrains MCP Proxy
• JetBrains IDE (IntelliJ IDEA, WebStorm, etc.)

💻 Usage Examples

Custom Tools Implementation

The plugin provides an extension point system that allows third - party plugins to implement their own MCP tools. Here's how to implement and register your custom tools:

1. Creating a Custom Tool

Create a class that extends AbstractMcpTool:

class MyCustomTool : AbstractMcpTool<MyArgs>() {
    override val name: String = "myCustomTool"
    override val description: String = "Description of what your tool does"

    override fun handle(project: Project, args: MyArgs): Response {
        // Implement your tool's logic here
        return Response.ok("Result")
    }
}

// Define your arguments data class
@Serializable
data class MyArgs(
    val param1: String,
    val param2: Int
)

2. Registering Your Tool

To register your tool, add it as an extension in your plugin.xml:

<idea-plugin>
    <!-- Your plugin config -->
    <depends>com.intellij.mcpServer</depends>
    
    <extensions defaultExtensionNs="com.intellij.mcpServer">
        <mcpTool implementation="com.example.MyCustomTool"/>
    </extensions>
</idea-plugin>

3. Tool Implementation Guidelines

Your tool implementation should follow these guidelines:

• Tool names should be descriptive and use lowercase with optional underscores.
• Create a data class for your tool's arguments that matches the expected JSON input.
• Use the Response class appropriately:
  • Response(result) for successful operations.
  • Response(error = message) for error cases.
• Use the provided Project instance for accessing IDE services.

📚 Documentation

How to Publish Update

1. Update settings.gradle.kts to provide a new version.
2. Create release on Github, the publishing task will be automatically triggered.

🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request.